Artificial Intelligence Generated Synthetic Image Data for Use with Machine Language Models

2. The method of claim 1, wherein said synthetic dataset is annotated with model training labels; and wherein said computer, responsive to generating said synthetic dataset, trains said ML model on said synthetic dataset.

3. The method of claim 1, wherein said generative model is a domain adaption model selected from list of model types consisting of adversarial-based domain adaptation models, divergence-based domain adaptation models, and reconstruction-based domain adaptation models.

4. The method of claim 1, wherein said formats are characterized, at least in part by attributes selected from a list consisting of temperature, ambient luminosity, relative humidity, contemporary rainfall quantity, time of day, cloud cover, soil moisture information, seed information, geographical region, and period of year.

5. The method of claim 1, wherein said data image analysis task is selected from a list consisting of crop identification, crop counting, crop quality assessment, and crop yield output estimation.

6. The method of claim 1, wherein said machine learning (ML) model is trained to solve data image analysis tasks selected from a list consisting of data content classification, data content segmentation, and data content regression.

9. The system of claim 8, wherein said synthetic dataset is annotated with model training labels; and wherein said instructions further cause the computer to, responsive to generating said synthetic dataset, train said ML model on said synthetic dataset.

10. The system of claim 8, wherein said generative model is a domain adaption model selected from list of model types consisting of adversarial-based domain adaptation models, divergence-based domain adaptation models, and reconstruction-based domain adaptation models.

11. The system of claim 8, wherein said formats are characterized, at least in part by attributes selected from a list consisting of temperature, ambient luminosity, relative humidity, contemporary rainfall quantity, time of day, cloud cover, soil moisture information, seed information, geographical region, and period of year.

12. The system of claim 8, wherein said data image analysis task is selected from a list consisting of crop identification, crop counting, crop quality assessment, and crop yield output estimation.

13. The system of claim 8, wherein said machine learning (ML) model is trained to solve data image analysis tasks selected from a list consisting of data content classification, data content segmentation, and data content regression.

16. The computer program product of claim 15, wherein said synthetic dataset is annotated with model training labels; and wherein said instructions further cause the computer to, responsive to generating said synthetic dataset, train said ML model on said synthetic dataset.

17. The computer program product of claim 15, wherein said generative model is a domain adaption model selected from list of model types consisting of adversarial-based domain adaptation models, divergence-based domain adaptation models, and reconstruction-based domain adaptation models.

18. The computer program product of claim 15, wherein said formats are characterized, at least in part by attributes selected from a list consisting of temperature, ambient luminosity, relative humidity, contemporary rainfall quantity, time of day, cloud cover, soil moisture information, seed information, geographical region, and period of year.

19. The computer program product of claim 15, wherein said machine learning (ML) model is trained to solve data image analysis tasks selected from a list consisting of data content classification, data content segmentation, and data content regression.